Quantitative interpretation of ToxTracker dose-response data for potency comparisons and mode-of-action determination.

ToxTracker is an in vitro mammalian stem cell-based reporter assay that detects activation of specific cellular signaling pathways (DNA damage, oxidative stress, and/or protein damage) upon chemical exposure using flow cytometry. Here we used quantitative methods to empirically analyze historical control data, and dose-response data across a wide range of reference chemicals. First, we analyzed historical control data to define a fold-change threshold for identification of a significant positive response. Next, we used the benchmark dose (BMD) combined-covariate approach for potency ranking of a set of more than 120 compounds; the BMD values were used for comparative identification of the most potent inducers of each reporter. Lastly, we used principal component analysis (PCA) to investigate functional and statistical relationships between the ToxTracker reporters. The PCA results, based on the BMD results for all substances, indicated that the DNA damage (Rtkn, Bscl2) and p53 (Btg2) reporters are functionally complementary and indicative of genotoxic stress. The oxidative stress (Srxn1 and Blvrb) and protein stress (Ddit3) reporters are independent indicators of cellular stress, and essential for toxicological profiling using the ToxTracker assay. Overall, dose-response modeling of multivariate ToxTracker data can be used for potency ranking and mode-of-action determination. In the future, IVIVE (in vitro to in vivo extrapolation) methods can be employed to determine in vivo AED (administered equivalent dose) values that can in turn be used for human health risk assessment.

Toxicological and pharmacological effects of pentacyclic triterpenes rich fraction obtained from the leaves of Mansoa hirsuta.

Mansoa hirsuta is a medicinal plant native to the Brazilian semi-arid region. This approach aimed to investigate the in vitro and in vivo toxicity and anti-inflammatory and analgesic actions of the M. hirsuta fraction (MHF). In vitro cell viability was assessed in 3T3 cells. In vivo, the acute toxicity test, a single dose of the MHF was administered. For the subchronic toxicity test, three doses of were administered for 30 days. Locomotion and motor coordination were assessed using open field and rota-rod. The anti-inflammatory activity was evaluated in carrageenan-induced paw edema and zymosan-induced air-pouch models. Myeloperoxidase (MPO) and total proteins were also measured. The antinociceptive activity MHF was determined using acid acetic-induced abdominal writhing and formalin models. In the cytotoxicity assay, MHF showed no significative impairment of cell viability and in the acute toxicity study, did not cause mortality or signs of toxicity. Repeated exposure to MHF did not cause relevant toxicological changes. The evaluation in the open field test showed that the MHF did not alter the locomotor activity and there was no change in motor coordination and balance of animals. MHF significantly reduced edema, MPO production, the migration of leukocytes and protein leakage. In addition, MHF reduced abdominal writhing and significantly inhibited the first and second stage of the formalin test. The results of this study indicated that MHF has an anti-inflammatory and analgesic potential without causing acute or subchronic toxic effects and it can be a promising natural source to be explored.

Animal Toxicology Studies on the Male Reproductive Effects of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin: Data Analysis and Health Effects Evaluation.

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is a well-known environmental poison that exist in the environment for many years. However, its effect on the male reproductive system has not been clearly stated. We conducted a meta-analysis of the effect of TCDD on the male reproductive system of rodents about TCDD. Results showed that that TCDD exposure reduced the testis weight (weighted mean difference [WMD]: -0.035, 95% confidence interval [CI]: -0.046 to -0.025), sperm count (WMD: -35, 95% CI: -42.980 to -27.019), and blood testosterone concentration (WMD: -0.171, 95% CI: -0.269 to -0.073). According to our research results, TCDD can cause damage to the male reproductive system of rodents through direct or indirect exposure. In order to further explore the potential hazards of TCDD to humans, more human-related research needs to be carried out.

A Review of the Main Considerations for Formulation Development in Preclinical Toxicology Studies.

The main considerations for the development of a formulation for preclinical safety assessment testing are explored. Intravenous, inhalation, oral and dermal dosing are given focus and although different dose routes do present their own individual challenges there are common themes that emerge. In each case it is necessary to maximise exposure to achieve high doses to satisfy regulatory requirements for safety assessment testing. This often involves producing formulations that are at the limits of solubility and maximum volumes possible for administration to different test species by the chosen route. It is concluded that for all routes it is important to thoroughly explore the stability of the test item in the proposed formulation matrix well ahead of dosing any animals, giving careful consideration to which excipients are used and what their underlying toxicity profile may be for the relevant preclinical species. In addition, determining the maximum achievable concentrations and weighing that against the maximum volumes that can be given by the chosen route in all the test species at an early stage will also give a read on whether it would be theoretically possible to achieve suitably high enough doses to support clinical work. Not doing so can cause delays in the development programme and may have ethical repercussions.

Analysis for data-derived extrapolation factors for procymidone.

The derivation of Chemical Specific Adjustment Factors (CSAFs) (IPCS, 2005; U.S. EPA, 2014) depends on the choice of appropriate dose metric. EPA and IPCS guidance was applied to derive a CSAF for developmental toxicity for procymidone (PCM). Although kinetic data were not available in humans at any dose, sufficient toxicokinetic data are available in a surrogate species, primates, and from chimeric mice with both rat and human liver cells to offer insights. Alternative approaches were explored in the derivation of the CSAG based on review of the available kinetic data. The most likely dosimetric adjustment is the Cmax based on the character of the critical effect - reduced anogenital distance and increased incidence of hypospadias in male rats, which likely occurs during a small window of time during development of the rat fetus. Cmax is also the default dosimeter from U.S. EPA (1991). However, in this case, the use of Cmax is also likely more conservative than the use of area under the curve (AUC), which otherwise is the default recommendation of the IPCS (2005). Despite human data, estimated tentative CSAF value is 0.48 (range, 0.22 to 0.74). The use of any of these values would be supported by the available data.

Case analysis of kinetics investigations in toxicity studies of pesticides to identify the nonlinearity of internal exposure and the influences of nonlinearity on the toxicological interpretation of pesticide residue.

The nonlinearity of internal exposure to 8 pesticides was investigated in toxicity studies using kinetics to identify nonlinearity visually and to investigate the influence of nonlinearity on toxicological evaluation. Data were obtained from risk assessment reports published by the Food Safety Commission (FSCJ). Nonlinearity was defined using 2 indicators: the lowest visual inflection point (LVIP) and the second lowest visual inflection point (SVIP) of kinetics by drawing a linear distribution chart. The area under the curve and 24-h urine concentrations were stable parameters used to identify the LVIP/SVIP. The sampling timing affected the blood concentrations, and the LVIP/SVIP was detected for 6 pesticides using the parent compounds or their metabolites as analytes. The subproportional nonlinearity was significant for these pesticides. The LVIP/SVIP values were consistent in the same species up to a 1-year period, but the values showed species-specific differences in several compounds. In all compounds found to be nonlinear, apical outcomes were observed at the SVIP or above. The presence of nonlinearity was recognized by the FSCJ. The recognition influenced their judgment of no-observed-adverse-effect levels (NOAELs) for carcinogenicity or health-based guidance values, indicating the importance of appropriate kinetics to identify the nonlinearity for toxicological evaluation of pesticide residue.

In silico prediction of mitochondrial toxicity of chemicals using machine learning methods.

Mitochondria are important organelles in human cells, providing more than 95% of the energy. However, some drugs and environmental chemicals could induce mitochondrial dysfunction, which might cause complex diseases and even worsen the condition of patients with mitochondrial damage. Some drugs have been withdrawn from the market due to their severe mitochondrial toxicity, such as troglitazone. Therefore, there is an urgent need to develop models that could accurately predict the mitochondrial toxicity of chemicals. In this paper, suitable data were obtained from literature and databases first. Then nine types of fingerprints were used to characterize these compounds. Finally, different algorithms were used to build models. Meanwhile, the applicability domain of the prediction models was defined. We have also explored the structural alerts of mitochondrial toxicity, which would be helpful for medicinal chemists to better predict mitochondrial toxicity and further optimize lead compounds.

Expanding the toxicologist's statistical toolbox: Using effect size estimation and dose-response modelling for holistic assessments instead of generic testing.

It is tempting to base (eco-)toxicological assay evaluation solely on statistical significance tests. The approach is stringent, objective and facilitates binary decisions. However, tests according to null hypothesis statistical testing (NHST) are thought experiments that rely heavily on assumptions. The generic and unreflected application of statistical tests has been called "mindless" by Gigerenzer. While statistical tests have an appropriate application domain, the present work investigates how unreflected testing may affect toxicological assessments. Dunnett multiple-comparison and Williams trend testing and their compatibility intervals are compared with dose-response-modelling in case studies, where data do not follow textbook behavior, nor behave as expected from a toxicological point of view. In such cases, toxicological assessments based only on p-values may be biased and biological evaluations based on plausibility may be prioritized. If confidence in a negative assay outcome cannot be established, further data may be needed for a robust toxicological assessment.

Long-term outcomes of psychoactive drug use in trauma patients: A multicenter patient-reported outcomes study.

INTRODUCTION: Psychoactive drug use (PDU) is reported in up to 40% of trauma patients and is associated with a higher rate of in-hospital complications. However, little is known about its long-term impact on trauma patients. We aimed to assess the long-term functional, mental, and psychosocial outcomes of PDU in trauma patients 6 to 12 months after injury. METHODS: Trauma patients with moderate to severe injuries (Injury Severity Score, >9) who had a toxicology screen upon admission to one of three level 1 trauma centers were contacted by phone 6 to 12 months postinjury. Psychoactive drug use was defined as the presence of a psychoactive, nonprescribed substance on toxicology screen including amphetamine, barbiturate, benzodiazepine, cannabinoid, methamphetamine, methadone, opioid, oxycodone, methylenedioxymethamphetamine (ecstasy), phencyclidine, tricyclic antidepressant, and cocaine. The interviews systematically evaluated functional limitations, social functioning, chronic pain, and mental health (posttraumatic stress disorder, depression, anxiety). Patients with a score of ≤47 on the Short-Form Health Survey version 2.0 social functioning subdomain were considered to have social dysfunction. Multivariable regression models were built to determine the independent association between PDU and long-term outcomes. RESULTS: Of the 1,699 eligible patients, 571 (34%) were included in the analysis, and 173 (30.3%) screened positive for PDU on admission. Patients with PDU were younger (median age [interquartile range], 43 [28-55] years vs. 66 [46-78] years, p < 0.001), had more penetrating injuries (8.7% vs. 4.3%, p = 0.036), and were less likely to have received a college education (41.3% vs. 54.5%, p = 0.004). After adjusting for patients' characteristics including the presence of a baseline psychiatric comorbidity, patients with PDU on admission were more likely to suffer from daily chronic pain, mental health disorders, and social dysfunction 6 to 12 months after injury. There was no difference in the functional limitations between patients with and without PDU. CONCLUSION: On the long term, PDU in trauma patients is strongly and independently associated with worse mental health, more chronic pain, and severe impairment in social functioning. A trauma hospitalization presents an opportunity to identify patients at risk and to mitigate the long-term impact of PDU on recovery. LEVEL OF EVIDENCE: Prognostic/epidemiologic, level III.

Hybrid algorithms for generating optimal designs for discriminating multiple nonlinear models under various error distributional assumptions.

Finding a model-based optimal design that can optimally discriminate among a class of plausible models is a difficult task because the design criterion is non-differentiable and requires 2 or more layers of nested optimization. We propose hybrid algorithms based on particle swarm optimization (PSO) to solve such optimization problems, including cases when the optimal design is singular, the mean response of some models are not fully specified and problems that involve 4 layers of nested optimization. Using several classical examples, we show that the proposed PSO-based algorithms are not models or criteria specific, and with a few repeated runs, can produce either an optimal design or a highly efficient design. They are also generally faster than the current algorithms, which are generally slow and work for only specific models or discriminating criteria. As an application, we apply our techniques to find optimal discriminating designs for a dose-response study in toxicology with 5 possible models and compare their performances with traditional and a recently proposed algorithm. In the supplementary material, we provide a R package to generate different types of discriminating designs and evaluate efficiencies of competing designs so that the user can implement an informed design.

Facilitation of risk assessment with evidence-based methods - A framework for use of systematic mapping and systematic reviews in determining hazard, developing toxicity values, and characterizing uncertainty.

Systematic review tools and approaches developed for clinical medicine are often difficult to apply "off the shelf" in order to meet the needs of chemical risk assessments. To address such, we propose an approach that can be used by practitioners for using evidence-based methods to facilitate the risk assessment process. The framework builds on and combines efforts conducted to date by a number of agencies and researchers; the novelty is in combining these efforts with a practical understanding of risk assessment, and translating such into a 'step-by-step' guide. The approach relies on three key components: problem formulation, systematic evidence mapping, and systematic review, applied using a stepwise approach. Unique to this framework is the consideration of exposure in selecting, prioritizing, and evaluating data (e.g., dose-relevance, routes of exposure, etc.). Using the proposed step-by-step process, critical appraisal of individual studies (e.g., formal and structured assessment of both relevance and reliability) and integration efforts are considered in context of specified risk assessment objectives (e.g., mode of action, dose-response) as well as chemical-specific considerations. The resulting framework provides a logical approach of how evidence-based methods can be used to facilitate risk assessment, and elevates the use of systematic methods beyond hazard identification to directly facilitating transparent and objective selection of candidate studies and/or datasets used to quantitatively characterize risk, and to better use the underlying process to inform the approaches used to develop toxicity values.

Use compatibility intervals in regulatory toxicology.

Recently it was recommended to avoid significance tests, in particular dichotomization into significant/non-significant on the basis of a p-value and a fixed 5% significance level (i.e. false positive rate). As an alternative, the interpretation of a suitable effect size and its compatibility interval is recommended, i.e. confidence intervals whose compatibility with the data, the assumptions, and the models is shown. This concept is used for the evaluation of assays in regulatory toxicology with special emphasis on the proof of hazard and proof of safety. Three case studies for multiple endpoints, multiple models and the consideration of historical controls illustrate the applicability of this concept. The corresponding software code for the open-source R project for statistical computing (www.r-project.org) is provided.

Enhanced normograms and pregnancy outcome analysis in nonhuman primate developmental toxicity studies.

The incidence of spontaneous pregnancy/infant losses is highly variable in long-tailed macaques (cynomolgus monkey), making it potentially difficult to ascertain test item-related effects in developmental toxicity studies. Therefore, pregnancy normograms had been developed by Jarvis et al. [1] to aid in the distinction of normal (e.g. test facility background) versus non-normal pregnancy outcomes. These normograms were mostly derived from embryo-fetal development studies and from PPND studies with a postnatal phase limited to seven days. However, the enhanced pre- and postnatal developmental (ePPND) study paradigm has essentially replaced these former study types. This work aims at providing enhanced normograms (e-normograms) in the context of regulatory ePPND studies. Survival functions for the prenatal phase (286 control pregnancies) and the postnatal phase (222 live infants) were estimated using the Kaplan-Meier estimator. Normograms were generated from survival curves and pseudo-study simulations. Data were available from two test facilities with comparable EU-compliant animal husbandry. Pregnancy duration/outcome as well as survival functions did not differ significantly between test facilities indicating that this husbandry system yields comparable developmental observations across different test facilities, at least in this NHP species. These novel e-normograms were developed for pregnant long-tailed macaques and provide an extended postnatal period up to three months, a new concept of separate normograms for the prenatal and the postnatal period, specific information on the perinatal phase events, a prediction of expected number of live infants for group size management, and the option to evaluate effects on pregnancy duration through distinction of live births and infant losses.

TOP: A deep mixture representation learning method for boosting molecular toxicity prediction.

At the early stages of the drug discovery, molecule toxicity prediction is crucial to excluding drug candidates that are likely to fail in clinical trials. In this paper, we presented a novel molecular representation method and developed a corresponding deep learning-based framework called TOP (the abbreviation of TOxicity Prediction). TOP integrates specifically designed data preprocessing methods, an RNN based on bidirectional gated recurrent unit (BiGRU), and fully connected neural networks for end-to-end molecular representation learning and chemical toxicity prediction. TOP can automatically learn a mixed molecular representation from not only SMILES contextual information that describes the molecule structure, but also physiochemical properties. Therefore, TOP can overcome the drawbacks of existing methods that use either of them, thus greatly promotes toxicity prediction accuracy. We conducted extensive experiments over 14 classic toxicity prediction tasks on three different benchmark datasets, including balanced and imbalanced ones. The results show that, with the help of the novel molecular representation method, TOP significantly outperforms not only three baseline machine learning methods, but also five state-of-the-art methods.

The Contribution of In Vivo Mammalian Studies to the Knowledge of Adverse Effects of Radiofrequency Radiation on Human Health.

The proliferation of cellular antennas and other radiofrequency radiation (RFR) generating devices of the last decades has led to more and more concerns about the potential health effects from RFR exposure. Since the 2011 classification as a possible carcinogen by the International Agency for Research on Cancer (IARC), more experimental studies have been published that support a causal association between RFR exposure and health hazards. As regard cancer risk, two long-term experimental studies have been recently published by the US National Toxicology Program (NTP) and the Italian Ramazzini Institute (RI). Despite important experimental differences, both studies found statistically significant increases in the development of the same type of very rare glial malignant tumors. In addition to carcinogenicity, reproductive organs might be particularly exposed, as well as sensitive to RFR. In this work, we reviewed the currently available evidence from in vivo studies on carcinogenicity and reproductive toxicity studies in order to summarize the contribution of experimental research to the prevention of the adverse effects of RFR on human health.

Statistical analysis of the reproducibility and predictive capacity of MCTT HCE™ eye irritation test, a me-too test method for OECD TG 492.

We aimed to conduct additional statistical analysis of the reproducibility and predictive capacity of MCTT HCE™ eye irritation test (EIT), a me-too test method for OECD TG 492 with the data generated during the validation study in which 30 reference chemicals were tested in three repeated runs by three independent laboratories. We evaluated the within-laboratory reproducibility (WLR) and the between-laboratory reproducibility (BLR) through tabulation and graphs and presented the concordance of eye irritancy prediction with 95% Wilson's confidence intervals (CIs). Also, the analyses of the Intra-Class Correlation Coefficient (ICC) and Bland-Altman plot were applied to confirm the reproducibility and the comparability, referring to the validated methods of OECD TG 492. Kappa analysis was also performed to check the degree of agreement of the within- and between-laboratory reproducibility and agreement between MCTT HCE™ EIT and the reference methods. We calculated the predictive capacity via misprediction over total prediction method. The predictive capacity (sensitivity, specificity, and accuracy) was presented with 95% of Wilson's CIs. Also, bootstrap resampling was performed to express the 95% CI by the simple percentile methods for 30 chemicals and 141 reference chemicals additionally tested. Collectively, WLR (92.2%) and BLR (93.3%) met the criteria of the performance standards (WLR ≥ 90% and BLR ≥ 85%), and the results of ICC analysis and the Bland-Altman plot suggested an acceptable WLR and BLR and comparability to other validated methods of OECD TG 492. Also, the predictive capacity results for the 30 reference chemicals confirmed the good performance of the MCTT HCE™ EIT by satisfying the criteria of sensitivity, specificity, and the accuracy stated in the PS. The bootstrap resampling method showed a predictive capacity, sufficiently satisfying the criteria stated in the Performance Standards.

Are honeybees suitable surrogates for use in pesticide risk assessment for non-Apis bees?

Historically, bee regulatory risk assessment for pesticides has centred on the European honeybee (Apis mellifera), primarily due to its availability and adaptability to laboratory conditions. Recently, there have been efforts to develop a battery of laboratory toxicity tests for a range of non-Apis bee species to directly assess the risk to them. However, it is not clear whether the substantial investment associated with the development and implementation of such routine screening will actually improve the level of protection of non-Apis bees. We argue, using published acute toxicity data from a range of bee species and standard regulatory exposure scenarios, that current first-tier honeybee acute risk assessment schemes utilised by regulatory authorities are protective of other bee species and further tests should be conducted only in cases of concern. We propose similar analysis of alternative exposure scenarios (chronic and developmental) once reliable data for non-Apis bees are available to expand our approach to these scenarios. In addition, we propose that in silico (simulation) approaches can then be used to address population-level effects in more field-realistic scenarios. Such an approach could lead to a protective, but also workable, risk assessment for non-Apis species while contributing to pollination security in agricultural landscapes around the globe. © 2019 Society of Chemical Industry.

Toxicity studies used in registration, evaluation, authorisation and restriction of chemicals (REACH): How accurately are they reported?

Toxicity studies on chemicals registered under the European Union's Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) regulation are provided as summaries instead of as a full study report. Because the registration data are used by regulatory agencies to identify chemicals of concern, the study summaries must accurately reflect the information in studies. A "study summary" should include sufficient information on the objectives, methods, results, and conclusions in the full study report in order for the relevance of the study to be determined. Sometimes a "robust study summary" is required, which should contain more detailed information to enable an independent assessment of the study. The aim of the present investigation is to examine how well published toxicity papers were reflected in study summaries submitted by registrants under REACH. Summaries of 20 published studies (peer-reviewed studies, including 1 abstract) were examined and broad categories of various types of observed differences were derived. The extent to which information in the published studies was reported, as well as how accurately the information was reflected, varied. How accurately the information was reflected also varied. Differences between the published studies and the summaries included simple typing errors, unclear and incomplete reporting, as well as the omission of information on, for example, study design, results, or interpretation of the results, which in some cases could be considered relevant for the risk assessment. This raises concerns regarding the accuracy of study summaries and their use for decision making. Moreover, the possibility for third parties to independently assess and scrutinize the summaries is limited. Considering that we rely on REACH registration data for chemical safety, all data used for risk assessment should be accessible for thorough examination and fully independent assessment. Integr Environ Assess Manag 2019;00:000-000. © 2019 SETAC.

Impact of Deuterium Substitution on the Pharmacokinetics of Pharmaceuticals.

OBJECTIVE: Stable heavy isotopes of hydrogen, carbon, and other elements have been incorporated into drug molecules, largely as tracers for quantitation during the drug development process. Studies involving the human use of drugs labeled with deuterium suggest that these compounds may offer some advantages when compared with their nondeuterated counterparts. Deuteration has gained attention because of its potential to affect the pharmacokinetic and metabolic profiles of drugs. Deutetrabenazine (Austedo, Teva Pharmaceutical Industries, Ltd) is the first deuterated drug to receive Food and Drug Administration approval. This deuterated form of the drug tetrabenazine is indicated for the treatment of chorea associated with Huntington's disease as well as tardive dyskinesia. Ongoing clinical trials suggest that a number of other deuterated compounds are being evaluated for the treatment of human diseases and not merely as research tools. DATA SOURCES: A search of the MEDLINE (1946 to present) database was undertaken using the Ovid interface. The search was conducted using the heading deuterium and then limited to Administration & Dosage, Adverse Effects, Pharmacokinetics, Pharmacology, Poisoning, Therapeutic Use, and Toxicity. STUDY SELECTION AND DATA EXTRACTION: All articles were reviewed and those with human information were included. Review articles were likewise interrogated for additional published human data. CONCLUSIONS: Deuterated compounds may, in some cases, offer advantages over nondeuterated forms, often through alterations in clearance. Deuteration may also redirect metabolic pathways in directions that reduce toxicities. The approval of additional deuterated compounds may soon follow. Clinicians will need to be familiar with the dosing, efficacy, potential side effects, and unique metabolic profiles of these new entities.

Bayesian additive adaptive basis tensor product models for modeling high dimensional surfaces: an application to high-throughput toxicity testing.

Many modern datasets are sampled with error from complex high-dimensional surfaces. Methods such as tensor product splines or Gaussian processes are effective and well suited for characterizing a surface in two or three dimensions, but they may suffer from difficulties when representing higher dimensional surfaces. Motivated by high throughput toxicity testing where observed dose-response curves are cross sections of a surface defined by a chemical's structural properties, a model is developed to characterize this surface to predict untested chemicals' dose-responses. This manuscript proposes a novel approach that models the multidimensional surface as a sum of learned basis functions formed as the tensor product of lower dimensional functions, which are themselves representable by a basis expansion learned from the data. The model is described and a Gibbs sampling algorithm is proposed. The approach is investigated in a simulation study and through data taken from the US EPA's ToxCast high throughput toxicity testing platform.